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Artificial Intelligence in Medicine and Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 22145

Special Issue Editor


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Guest Editor

Special Issue Information

Dear Colleagues,

Artificial intelligence technology is a broad cross-cutting frontier subject. In recent years, computer software and hardware technologies have developed rapidly. Artificial intelligence is a branch of computer science that involves the research and application of intelligent machines. Its main goal is to study certain intellectual abilities of computers that imitate the human brain. The technology involves intelligent expert system, language processing, intelligent data retrieval, intelligent control and many other aspects, and has made outstanding achievements.

In recent years, artificial intelligence technology has been widely used in the field of medical and health, such as disease prediction and diagnosis and treatment, drug research and development, etc., which have greatly improved medical service capabilities, effectively alleviated the above problems, and promoted the reform and development of medical health. The ever-increasing demand for medical and health care has led to unprecedented development of smart health. Compared with traditional medical care, smart health has the characteristics of personalized health big data diagnosis and treatment, multi-participant cooperation and collaboration, and full-process intelligence in the medical process. Vigorously promoting "artificial intelligence + medical health" can give new vitality to the medical industry, and will effectively promote the innovative supply of medical services and the open sharing of information resources.

Potential topics include, but are not limited to:

  • Disease diagnosis using machine learning;
  • Medical image processing and intelligent perception;
  • Medical data modeling based on cluster computing;
  • Protein structure prediction using high-performance computing;
  • Sequencing data processing on the Internet of Medical Things platform;
  • Expression profiling data analysis using convolutional neural networks.

Dr. Kelvin Wong
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • medical image processing
  • data processing
  • internet of medical things

Published Papers (6 papers)

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Research

17 pages, 4435 KiB  
Article
ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment
by Baizheng Wu, Chengxin Pang, Xinhua Zeng and Xing Hu
Appl. Sci. 2022, 12(23), 11978; https://doi.org/10.3390/app122311978 - 23 Nov 2022
Cited by 8 | Viewed by 3201
Abstract
Corona Virus Disease 2019 (COVID-19) poses a significant threat to human health and safety. As the core of the prevention and control of COVID-19, the health and safety of medical and nursing personnel are extremely important, and the standardized use of medical personal [...] Read more.
Corona Virus Disease 2019 (COVID-19) poses a significant threat to human health and safety. As the core of the prevention and control of COVID-19, the health and safety of medical and nursing personnel are extremely important, and the standardized use of medical personal protective equipment can effectively prevent cross-infection. Due to the existence of severe occlusion and overlap, traditional image processing methods struggle to meet the demand for real-time detection. To address these problems, we propose the ME-YOLO model, which is an improved model based on the one-stage detector. To improve the feature extraction ability of the backbone network, we propose a feature fusion module (FFM) merged with the C3 module, named C3_FFM. To fully retain the semantic information and global features of the up-sampled feature map, we propose an up-sampling enhancement module (USEM). Furthermore, to achieve high-accuracy localization, we use EIoU as the loss function of the border regression. The experimental results demonstrate that ME-YOLO can better balance performance (97.2% mAP) and efficiency (53 FPS), meeting the requirements of real-time detection. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
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18 pages, 3693 KiB  
Article
A Progressively Expanded Database for Automated Lung Sound Analysis: An Update
by Fu-Shun Hsu, Shang-Ran Huang, Chien-Wen Huang, Yuan-Ren Cheng, Chun-Chieh Chen, Jack Hsiao, Chung-Wei Chen and Feipei Lai
Appl. Sci. 2022, 12(15), 7623; https://doi.org/10.3390/app12157623 - 28 Jul 2022
Cited by 7 | Viewed by 3805
Abstract
We previously established an open-access lung sound database, HF_Lung_V1, and developed deep learning models for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS) detection. The amount of data used for training contributes to model accuracy. In this study, we collected [...] Read more.
We previously established an open-access lung sound database, HF_Lung_V1, and developed deep learning models for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS) detection. The amount of data used for training contributes to model accuracy. In this study, we collected larger quantities of data to further improve model performance and explored issues of noisy labels and overlapping sounds. HF_Lung_V1 was expanded to HF_Lung_V2 with a 1.43× increase in the number of audio files. Convolutional neural network–bidirectional gated recurrent unit network models were trained separately using the HF_Lung_V1 (V1_Train) and HF_Lung_V2 (V2_Train) training sets. These were tested using the HF_Lung_V1 (V1_Test) and HF_Lung_V2 (V2_Test) test sets, respectively. Segment and event detection performance was evaluated. Label quality was assessed. Overlap ratios were computed between inhalation, exhalation, CAS, and DAS labels. The model trained using V2_Train exhibited improved performance in inhalation, exhalation, CAS, and DAS detection on both V1_Test and V2_Test. Poor CAS detection was attributed to the quality of CAS labels. DAS detection was strongly influenced by the overlapping of DAS with inhalation and exhalation. In conclusion, collecting greater quantities of lung sound data is vital for developing more accurate lung sound analysis models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
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18 pages, 2895 KiB  
Article
Using a Video Device and a Deep Learning-Based Pose Estimator to Assess Gait Impairment in Neurodegenerative Related Disorders: A Pilot Study
by Andrea Zanela, Tommaso Schirinzi, Nicola Biagio Mercuri, Alessandro Stefani, Cristian Romagnoli, Giuseppe Annino, Vincenzo Bonaiuto and Rocco Cerroni
Appl. Sci. 2022, 12(9), 4642; https://doi.org/10.3390/app12094642 - 5 May 2022
Cited by 9 | Viewed by 2198
Abstract
As the world’s population is living longer, age-related neurodegenerative diseases are becoming a more significant global issue. Neurodegenerative diseases cause worsening motor, cognitive and autonomic dysfunction over time and reduce functional abilities required for daily living. Compromised motor performance is one of the [...] Read more.
As the world’s population is living longer, age-related neurodegenerative diseases are becoming a more significant global issue. Neurodegenerative diseases cause worsening motor, cognitive and autonomic dysfunction over time and reduce functional abilities required for daily living. Compromised motor performance is one of the first and most evident manifestations. In the case of Parkinson’s disease, these impairments are currently evaluated by experts through the use of rating scales. Although this method is widely used by experts worldwide, it includes subjective and error-prone motor examinations that also fail in the characterization of symptoms’ fluctuations. The aim of this study is to evaluate whether artificial intelligence techniques can be used to objectively assess gait impairment in subjects with Parkinson’s disease. This paper presents the results of a cohort of ten subjects, five with a Parkinson’s disease diagnosis at different degrees of severity. We experimentally demonstrate good effectiveness of the proposed system in extracting the main features concerning people’s gait during the standard tests that clinicians use to assess the burden of disease. This system can offer neurologists, through accurate and objective data, a second opinion or a suggestion to reconsider score assignment. Thanks to its simplicity, tactful and non-intrusive approach and clinical-grade accuracy, it can be adopted on an ongoing basis even in environments where people usually live and work. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
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16 pages, 8803 KiB  
Article
Classification of Distal Growth Plate Ossification States of the Radius Bone Using a Dedicated Ultrasound Device and Machine Learning Techniques for Bone Age Assessments
by Lukas Brausch, Ruth Dirksen, Christoph Risser, Martin Schwab, Carole Stolz, Steffen Tretbar, Tilman Rohrer and Holger Hewener
Appl. Sci. 2022, 12(7), 3361; https://doi.org/10.3390/app12073361 - 25 Mar 2022
Cited by 3 | Viewed by 6734
Abstract
X-ray imaging, based on ionizing radiation, can be used to determine bone age by examining distal growth plate fusion in the ulna and radius bones. Legal age determination approaches based on ultrasound signals exist but are unsuitable to reliably determine bone age. We [...] Read more.
X-ray imaging, based on ionizing radiation, can be used to determine bone age by examining distal growth plate fusion in the ulna and radius bones. Legal age determination approaches based on ultrasound signals exist but are unsuitable to reliably determine bone age. We present a low-cost, mobile system that uses one-dimensional ultrasound radio frequency signals to obtain a robust binary classifier enabling the determination of bone age from data of girls and women aged 9 to 24 years. These data were acquired as part of a clinical study conducted with 148 subjects. Our system detects the presence or absence of the epiphyseal plate by moving ultrasound array transducers along the forearm, measuring reflection and transmission signals. Even though classical digital signal processing methods did not achieve a robust classifier, we achieved an F1 score of approximately 87% for binary classification of completed bone growth with machine learning approaches, such as the gradient boosting machine method CatBoost. We demonstrate that our ultrasound system can classify the fusion of the distal growth plate of the radius bone and the completion of bone growth with high accuracy. We propose a non-ionizing alternative to established X-ray imaging methods for this purpose. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
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16 pages, 3171 KiB  
Article
A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network
by Huilin Ge, Yuewei Dai, Zhiyu Zhu and Biao Wang
Appl. Sci. 2021, 11(24), 11588; https://doi.org/10.3390/app112411588 - 7 Dec 2021
Cited by 4 | Viewed by 2578
Abstract
Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: [...] Read more.
Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
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10 pages, 1371 KiB  
Article
A New Inspiration in Bionic Shock Absorption Midsole Design and Engineering
by Hai-Bin Yu, Rui Zhang, Guo-Long Yu, Hai-Tao Wang, Dao-Chen Wang, Wei-Hsun Tai and Jian-Long Huang
Appl. Sci. 2021, 11(20), 9679; https://doi.org/10.3390/app11209679 - 17 Oct 2021
Cited by 4 | Viewed by 2217
Abstract
Inspired by the performance of the ostrich in terms of loading and high-speed moving ability, the purpose of this study was to design a structure and material on the forefoot and heel of the middle soles of sports shoes based on the high [...] Read more.
Inspired by the performance of the ostrich in terms of loading and high-speed moving ability, the purpose of this study was to design a structure and material on the forefoot and heel of the middle soles of sports shoes based on the high cushioning quality of the ostrich toe pad by applying bionic engineering technology. The anatomical dissection method was used to analyze the ostrich foot characteristics. The structure and material of the bionic shock absorption midsole were designed according to the principles of bionic engineering and reverse engineering. F-Scan and numerical simulation were used to evaluate the bionic shock absorption midsole performance. The results showed that the bionic shock absorption midsole decreased the peak pressure (6.04–12.27%), peak force (8.62–16.03%), pressure–time integral (3.06–12.66%), and force–time integral (4.06–10.58%) during walking and brisk walking. In this study, the biomechanical effects to which the bionic shock absorption midsole structure was subjected during walking and brisk walking exercises were analyzed. The bionic midsole has excellent shock resistance. It is beneficial for the comfort of the foot during exercise and might reduce the risk of foot injuries during exercise. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
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